Economic parameters cause the value of the financial instruments, such as mortgages and mortgage-backed securities, to fluctuate. Therefore, financial institutions, such as banks and brokerage firms, regularly have to determine the value of financial instruments. The processing and time requirements necessary to perform these valuations are substantial. Moreover, it is important to fit a model to the underlying economic parameter so that the financial institution is able to manage the risk of its portfolio by determining the likely range of future prices and value at risk, among other variables.
Therefore, financial institutions generally apply simulation models to a financial instrument or collections of instruments. These simulation models are usually complex algorithms commonly implemented in a computer programming language. A financial institution may have multiple applications that require analytic results generated by the same simulation model or similar simulation models. Although multiple applications within the institution may use the same model, the code that provides the functionality for the simulation model is typically embedded within each application. Moreover, the model in each application is often coded with incompatible computer programming languages and/or data structures. Thus, a user of a first application often is not able to use the same model code from the first application in a second application. This approach is inefficient in that it requires that the same simulation model be coded multiple times in different applications within the same institution and does not allow for simulation model codes to be reused across the various applications.
Another approach, which results in an inflexible data framework, requires expensive external coordination and integration relationships. With this approach, financial institutions utilize an enterprise-wide integrated application solution, such as Oracle, and/or SAS running on an IBM mainframe, from an external vendor. All modeling applications in the enterprise-wide solution are coded using the same data structures. Although this approach provides a common data framework, it requires that all previously used modeling applications be standardized. This, in turn, may constrain the flexibility of the previously used data framework(s) in the financial institution.
In addition to valuing individual financial instruments, many financial institutions also are often required to rapidly assess large portfolios containing multiple financial instruments. However, many financial institutions do not employ comprehensive distributive processing techniques in their financial instrument valuation. Given the complexity of simulation models that assess large portfolios, the institution's enterprise-wide computer processing resources may be significantly drained and may result in inefficiencies in information and resource sharing. Therefore, the enterprise-wide application approach does not only reduce flexibility in the institution's data framework, it precludes optimal use of network resources when solving large complex resource intensive calculations.